Project 1: History of Philosophy

Introduction

This report aims to explore the features, differences or similarities of texts of philosophers or philosophy schools using several different methodologies.

Install and load libraries:

packages.used <- c("tidyverse", "tm", "wordcloud", "RColorBrewer", "tidytext",
                "ggplot2", "knitr", "sentimentr", "syuzhet", "stringr", "sqldf", 
                "gplots", "factoextra")
packages.needed <- setdiff(packages.used, 
                        intersect(installed.packages()[,1], 
                                  packages.used))
if(length(packages.needed)>0){
  install.packages(packages.needed, dependencies = TRUE,
                   repos='http://cran.us.r-project.org')
}

library(tidyverse)
library(tm)
library(wordcloud)
library(RColorBrewer)
library(tidytext)
library(ggplot2)
library(knitr)
library(sentimentr)
library(syuzhet)
library(stringr)
library(sqldf)
library(gplots)
library(factoextra)
source("../lib/wordcloud_function.R")
source("../lib/emo_fn_plot.R")

Wordcloud part

Explore the most frequently used words in the overall texts:

ph <- read.csv("../data/philosophy_data.csv", header = TRUE)
ph_cor <- Corpus(VectorSource(ph$tokenized_txt))
ph_tidy <- ph_cor %>% 
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(removeWords, c("one", "will", "must", "can", "since", "just", "yet", "might", "however", "two", "may", stopwords("english"))) %>%
  tm_map(stripWhitespace) %>%
  TermDocumentMatrix() %>% 
  tidy()
ph_overall <- ph_tidy %>% 
  group_by(term) %>% 
  summarise(frequency = sum(count)) %>% 
  arrange(desc(frequency))
kable(ph_overall[1:10,])

term	frequency
things	17482
also	16781
man	16152
time	15307
even	14461
first	13880
now	13041
say	12856
way	12854
nature	12796

Above are the top 10 frequent words in the overall texts.

Inspect an overall wordcloud:

wordcloud(ph_overall$term, ph_overall$frequency,
          scale = c(3,0.1),
          max.words = 150,
          min.freq = 1,
          random.order = FALSE,
          rot.per = 0.35,
          random.color = FALSE,
          colors=brewer.pal(12,"Paired"))

We can see that there are words like “things”, “nature”, “human”, “knowledge”, “ideas”, “subject”, etc.

Identify interesting words for each sentence. Compute TF-IDF weighted document-term matrices and generate wordcloud based on [TF-IDF].

ph_all <- ph_cor %>% 
  tm_map(removeNumbers) %>%
  tm_map(removePunctuation) %>%
  tm_map(removeWords, c("one", "will", "must", "can", "since", "just", "yet", "might", "however", "two", "may", stopwords("english"))) %>%
  tm_map(stripWhitespace) 
ph_dtm <- DocumentTermMatrix(ph_all,
                          control = list(weighting = function(x) weightTfIdf(x,normalize=FALSE)))
ph_dtm = removeSparseTerms(ph_dtm , 0.99)
ph_dtm_tidy <- tidy(ph_dtm)
freq <- sqldf("select term, sum(count) as ct
      from ph_dtm_tidy
      group by term
      order by sum(count) desc")
wordcloud(freq$term, freq$ct,
          scale = c(3,0.1),
          max.words = 100,
          min.freq = 1,
          random.order = FALSE,
          rot.per = 0.3,
          random.color = FALSE,
          colors=brewer.pal(12,"Accent"))

There are words like “god”, “know”, “nature”, “true”, “think”, “power”, “mind”, “life”, “existence”, “subject”, “knowledge”, etc. We know that philosophy is a study of general and fundamental questions, such as life, mind, knowledge, or other abstract topics.

Inspect wordcloud of each school of philosophy, compare them.

ph_school <- ph %>% split(.$school)

par(mfrow=c(1,2))
wordcloud_fn(ph_school[1], names(ph_school[1]), "Dark2")
wordcloud_fn(ph_school[2], names(ph_school[2]), "Blues")

wordcloud_fn(ph_school[3], names(ph_school[3]), "Reds")
wordcloud_fn(ph_school[4], names(ph_school[4]), "PuOr")

wordcloud_fn(ph_school[5], names(ph_school[5]), "BuGn")
wordcloud_fn(ph_school[6], names(ph_school[6]), "Set2")

wordcloud_fn(ph_school[7], names(ph_school[7]), "RdGy")
wordcloud_fn(ph_school[8], names(ph_school[8]), "PiYG")

wordcloud_fn(ph_school[9], names(ph_school[9]), "BrBG")
wordcloud_fn(ph_school[10], names(ph_school[10]), "Oranges")

wordcloud_fn(ph_school[11], names(ph_school[11]), "Spectral")
wordcloud_fn(ph_school[12], names(ph_school[12]), "RdYlBu")

par(mfrow=c(1,1))
wordcloud_fn(ph_school[13], names(ph_school[13]), "RdYlGn")

We can see that each different philosophy schools has obviously different key words. For example, for capitalism, there are “price”, “money”, “quantity”, “wages”, “tax”, “trade”, “goods”, “expense”, “market”, which makes sense. And for feminism, there are “women”, “woman”, “freedom”, “role”, “mind”, “work”, “rights”, “slave”, “white”, “black”, which also makes sense. There are also some similarities among those different schools. Almost all of them contains words like “mind”, “things”, “nature”, “people”, “god”, which are some features of philosophy study.

Explore the emotions

We can see that there are some authors in the same school. I’m curious that if authors in the same school have similar patterns of emotions in their sentences, as well as sentence length variation. Let’s choose the school of analytic to analyze.

sqldf("select DISTINCT school, author
      from ph 
      order by school")

ph_analytic <- ph_school$analytic
word_count <- str_count(ph_analytic$sentence_str, "\\w+")
emo_analytic <- diag(1/(word_count+0.01)) %*%  as.matrix(get_nrc_sentiment(ph_analytic$sentence_str))
ph_emo_ana <- cbind(ph_analytic[ ,1:5], word_count, emo_analytic)
ph_emo_ana <- sqldf(
"select *, row_number() over(partition by author) as sent_id
      from ph_emo_ana
      order by author"
)

par(mfrow=c(2,1), mar=c(1,0,2,0), bty="n", xaxt="n", yaxt="n", font.main=1)
emo_fn(df_a = ph_emo_ana, author_v = "Russell", author = "Russell")
emo_fn(df_a = ph_emo_ana, author_v = "Moore", author = "Moore")

emo_fn(df_a = ph_emo_ana, author_v = "Wittgenstein", author = "Wittgenstein")
emo_fn(df_a = ph_emo_ana, author_v = "Lewis", author = "Lewis")

emo_fn(df_a = ph_emo_ana, author_v = "Quine", author = "Quine")
emo_fn(df_a = ph_emo_ana, author_v = "Popper", author = "Popper")

emo_fn(df_a = ph_emo_ana, author_v = "Kripke", author = "Kripke")

As we can see from above plots, the authors have some similarities in emotions. There are only very small part of orange (disgust) and yellow(sadness) lines in all plots. They also have some different changes of emotions. Like for Wittgenstein, there are many red lines (anger); and for Popper, there are many light green (anticipation) lines; for Quine, there are many dark green (trust) lines. For change of sentence length, there are also some differences among those authors of analytic school. Wittgenstein, Lewis, and Moore seem to have more changes of sentence length than the others.

Clustering of emotions

heatmap.2(cor(ph_emo_ana %>% select(anger:trust)), 
          scale = "none", 
          col = bluered(100), margin=c(6, 6), key=F,
          trace = "none", density.info = "none")

If I choose authors from several different schools, will the authors from the same school be clustered together? This is a very interesting test. I choose authors from analytic, capitalism, feminism and empiricism.

ph_capital <- ph_school$capitalism
word_count_cap <- str_count(ph_capital$sentence_str, "\\w+")
emo_capital <- diag(1/(word_count_cap+0.01)) %*% as.matrix(get_nrc_sentiment(ph_capital$sentence_str))
ph_emo_cap <- cbind(ph_capital[ ,1:5], emo_capital)

ph_feminism <- ph_school$feminism
word_count_fem <- str_count(ph_feminism$sentence_str, "\\w+")
emo_feminism <- diag(1/(word_count_fem+0.01)) %*% as.matrix(get_nrc_sentiment(ph_feminism$sentence_str))
ph_emo_fem <- cbind(ph_feminism[ ,1:5], emo_feminism)

ph_empiricism <- ph_school$empiricism
word_count_emp <- str_count(ph_empiricism$sentence_str, "\\w+")
emo_empiricism <- diag(1/(word_count_emp+0.01)) %*% as.matrix(get_nrc_sentiment(ph_empiricism$sentence_str))
ph_emo_emp <- cbind(ph_empiricism[ ,1:5], emo_empiricism)

ph_emo_all <- rbind(ph_emo_ana[,c(-6,-17)], ph_emo_cap, ph_emo_fem, ph_emo_emp)

Summary of emotions for each of the four school:

par(mfrow=c(2,2), mar=c(4, 6, 2, 1))
col.use=c("red2", "darkgoldenrod1", 
            "chartreuse3", "blueviolet",
            "darkgoldenrod2", "dodgerblue3", 
            "darkgoldenrod1", "darkgoldenrod1")
emo_means_ana=colMeans(select(ph_emo_ana, anger:trust)>0.01)
barplot(emo_means_ana[order(emo_means_ana)], las=2, col=col.use[order(emo_means_ana)], horiz=T, main = "analytic")
emo_means_cap=colMeans(select(ph_emo_cap, anger:trust)>0.01)
barplot(emo_means_cap[order(emo_means_cap)], las=2, col=col.use[order(emo_means_cap)], horiz=T, main = "capitalism")
emo_means_fem=colMeans(select(ph_emo_fem, anger:trust)>0.01)
barplot(emo_means_fem[order(emo_means_fem)], las=2, col=col.use[order(emo_means_fem)], horiz=T, main = "feminism")
emo_means_emp=colMeans(select(ph_emo_emp, anger:trust)>0.01)
barplot(emo_means_emp[order(emo_means_emp)], las=2, col=col.use[order(emo_means_emp)], horiz=T, main = "empiricism")

It seems that the emotions of the 4 different schools are pretty similar based on means. The top 2 most outstanding emotions for all these schools are trust and anticipation, which are positive.

Clustering

set.seed(1)
emo_summary <- tbl_df(ph_emo_all) %>% 
  group_by(author) %>% 
  summarise(
    anger=mean(anger),
    anticipation=mean(anticipation),
    disgust=mean(disgust),
    fear=mean(fear),
    joy=mean(joy),
    sadness=mean(sadness),
    surprise=mean(surprise),
    trust=mean(trust)
  )
emo_summary <- as.data.frame(emo_summary)
rownames(emo_summary) <- as.character((emo_summary[,1]))
km <- kmeans(emo_summary[,-1], iter.max=200, 4)
fviz_cluster(km, 
             stand=F, repel= TRUE,
             data = emo_summary[, -1], xlab="", xaxt="n", ylab = "",
             show.clust.cent=FALSE)

sqldf("select DISTINCT school, author
      from ph 
      where school in ('analytic', 'capitalism', 'feminism', 'empiricism')
      order by school")